Market Basket Analysis

Executive Summary

Introduction

Danielle has asked the team to perform a market basket analysis to help Blackwell’s board of directors better understand the clientele that Electronidex is currently serving and if Electronidex would be an optimal acquisition.

A dataset of transactions has been provided. The dataset contains 9835 transactions and 125 different products over a 30-day period, or about 327 transactions a day. This tells us the retailer is neither large, nor small.

Results and conclussions

After the analysis, we conclude Electronidex’s sales are to be categorized in two forms: retail (B2C) and corporate (B2B). Had we had this information previous to our analysis, it would have saved time in the exploration phase.

Interesting patterns and item relationships found: Retail: >

Would Blackwell benefit in selling Electronidex’s items?

Limitations and observations:

Properties of the dataset: 1. The iMac is the product most bought, in 20% of all transactions. This high number stands out considering the large variety of products, especially being the iMac a pricey product. If this number is representative of all sales throughout the year, then Electronidex is potentially profitable. 2. The mean of items bought per transaction is almost 5. Logically, I would say most people in the real world would buy 1 or 2 items per transactions most frequently, in an eletronics store.

Recommendations

Blackwell should acquire Electronidex If Blackwell does acquire Electronidex, do you have any recommendations for Blackwell? (Ex: cross-selling #items, sale promotions, should they remove items, etc.)

Preprocessing

transactions as itemMatrix in sparse format with
 9835 rows (elements/itemsets/transactions) and
 125 columns (items) and a density of 0.03506172 

most frequent items:
                    iMac                HP Laptop CYBERPOWER Gamer Desktop 
                    2519                     1909                     1809 
           Apple Earpods        Apple MacBook Air                  (Other) 
                    1715                     1530                    33622 

element (itemset/transaction) length distribution:
sizes
   0    1    2    3    4    5    6    7    8    9   10   11   12   13   14 
   2 2163 1647 1294 1021  856  646  540  439  353  247  171  119   77   72 
  15   16   17   18   19   20   21   22   23   25   26   27   29   30 
  56   41   26   20   10   10   10    5    3    1    1    3    1    1 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   2.000   3.000   4.383   6.000  30.000 

includes extended item information - examples:
                            labels
1 1TB Portable External Hard Drive
2 2TB Portable External Hard Drive
3                   3-Button Mouse
                            labels
1 1TB Portable External Hard Drive
2 2TB Portable External Hard Drive
3                   3-Button Mouse
4 3TB Portable External Hard Drive
5           5TB Desktop Hard Drive
6                      Acer Aspire

Feature Engineering

trans_df$nitems <- nitems
trans_df$laptops <- trans_df[, which(colnames(trans_df) == "LG Touchscreen Laptop")] + 
    trans_df[, which(colnames(trans_df) == "Acer Aspire")] + trans_df[, which(colnames(trans_df) == 
    "HP Laptop")] + trans_df[, which(colnames(trans_df) == "ASUS Chromebook")] + 
    trans_df[, which(colnames(trans_df) == "Apple Macbook Pro")] + trans_df[, 
    which(colnames(trans_df) == "Apple MacBook Air")] + trans_df[, which(colnames(trans_df) == 
    "Dell Laptop")] + trans_df[, which(colnames(trans_df) == "Eluktronics Pro Gaming Laptop")] + 
    trans_df[, which(colnames(trans_df) == "Alienware AW17R4-7345SLV-PUS 17\" Laptop")] + 
    trans_df[, which(colnames(trans_df) == "HP Notebook Touchscreen Laptop PC")]
trans_df$desktop <- trans_df[, which(colnames(trans_df) == "Lenovo Desktop Computer")] + 
    trans_df[, which(colnames(trans_df) == "iMac")] + trans_df[, which(colnames(trans_df) == 
    "HP Desktop")] + trans_df[, which(colnames(trans_df) == "ASUS Desktop")] + 
    trans_df[, which(colnames(trans_df) == "Dell Desktop")] + trans_df[, which(colnames(trans_df) == 
    "Intel Desktop")] + trans_df[, which(colnames(trans_df) == "Acer Desktop")] + 
    trans_df[, which(colnames(trans_df) == "CYBERPOWER Gamer Desktop")] + trans_df[, 
    which(colnames(trans_df) == "Dell 2 Desktop")]
trans_df$tablet <- trans_df[, which(colnames(trans_df) == "iPad")] + trans_df[, 
    which(colnames(trans_df) == "iPad Pro")] + trans_df[, which(colnames(trans_df) == 
    "Fire HD Tablet")] + trans_df[, which(colnames(trans_df) == "Samsung Galaxy Tab")] + 
    trans_df[, which(colnames(trans_df) == "Kindle")]
trans_df$printer <- trans_df$`Epson Printer` + trans_df$`HP Wireless Printer` + 
    trans_df$`Canon Office Printer` + trans_df$`Brother Printer` + trans_df$`DYMO Label Manker`
trans_df$nmain <- trans_df$printer + trans_df$laptops + trans_df$desktop + trans_df$tablet
trans_df$ncomp <- trans_df$nitems - trans_df$nmain
trans_df$value <- 10 * trans_df$nmain + trans_df$ncomp

Splitting dataframe between corporates and retailers

## Creating rules via apriori algorithm

Rules for products in corporate transactions

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen
       0.01    0.1    1 none FALSE            TRUE       5    0.01      2
 maxlen target   ext
     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 58 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[125 item(s), 5835 transaction(s)] done [0.00s].
sorting and recoding items ... [96 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 done [0.00s].
writing ... [1752 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
set of 1607 rules 
set of 1752 rules

rule length distribution (lhs + rhs):sizes
   2    3    4 
1032  672   48 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.000   2.000   2.000   2.438   3.000   4.000 

summary of quality measures:
    support          confidence           lift            count      
 Min.   :0.01011   Min.   :0.02775   Min.   :0.6424   Min.   : 59.0  
 1st Qu.:0.01148   1st Qu.:0.12435   1st Qu.:1.0782   1st Qu.: 67.0  
 Median :0.01422   Median :0.20365   Median :1.2491   Median : 83.0  
 Mean   :0.01827   Mean   :0.23648   Mean   :1.3030   Mean   :106.6  
 3rd Qu.:0.01971   3rd Qu.:0.32164   3rd Qu.:1.4580   3rd Qu.:115.0  
 Max.   :0.12734   Max.   :0.69388   Max.   :2.7337   Max.   :743.0  

mining info:
       data ntransactions support confidence
 trans_corp          5835    0.01       0.01
     lhs                           rhs                        support   
[1]  {HP Laptop}                => {iMac}                     0.12733505
[2]  {iMac}                     => {HP Laptop}                0.12733505
[3]  {Lenovo Desktop Computer}  => {iMac}                     0.09905741
[4]  {iMac}                     => {Lenovo Desktop Computer}  0.09905741
[5]  {CYBERPOWER Gamer Desktop} => {iMac}                     0.09562982
[6]  {iMac}                     => {CYBERPOWER Gamer Desktop} 0.09562982
[7]  {Dell Desktop}             => {iMac}                     0.09203085
[8]  {iMac}                     => {Dell Desktop}             0.09203085
[9]  {ViewSonic Monitor}        => {iMac}                     0.08157669
[10] {iMac}                     => {ViewSonic Monitor}        0.08157669
     confidence lift     count
[1]  0.4267662  1.132930 743  
[2]  0.3380346  1.132930 743  
[3]  0.4355690  1.156299 578  
[4]  0.2629663  1.156299 578  
[5]  0.3798502  1.008383 558  
[6]  0.2538672  1.008383 558  
[7]  0.4372964  1.160885 537  
[8]  0.2443130  1.160885 537  
[9]  0.4803229  1.275107 476  
[10] 0.2165605  1.275107 476  
     lhs                          rhs            support confidence     lift count
[1]  {Dell Desktop,                                                               
      Lenovo Desktop Computer,                                                    
      ViewSonic Monitor}       => {iMac}      0.01165381  0.6938776 1.842027    68
[2]  {Apple Magic Keyboard,                                                       
      ASUS Monitor}            => {iMac}      0.01148243  0.6767677 1.796606    67
[3]  {Acer Aspire,                                                                
      iMac,                                                                       
      ViewSonic Monitor}       => {HP Laptop} 0.01045416  0.6630435 2.222205    61
[4]  {Acer Desktop,                                                               
      HP Laptop,                                                                  
      ViewSonic Monitor}       => {iMac}      0.01079692  0.6562500 1.742138    63
[5]  {ASUS 2 Monitor,                                                             
      Dell Desktop}            => {iMac}      0.01525278  0.6449275 1.712080    89
[6]  {ASUS Monitor,                                                               
      Lenovo Desktop Computer} => {iMac}      0.01645244  0.6442953 1.710402    96
[7]  {Acer Desktop,                                                               
      ASUS 2 Monitor}          => {iMac}      0.01079692  0.6428571 1.706584    63
[8]  {ASUS Monitor,                                                               
      ViewSonic Monitor}       => {iMac}      0.01388175  0.6428571 1.706584    81
[9]  {ASUS Monitor,                                                               
      Dell Desktop}            => {iMac}      0.01336761  0.6393443 1.697258    78
[10] {Acer Desktop,                                                               
      iMac,                                                                       
      ViewSonic Monitor}       => {HP Laptop} 0.01079692  0.6363636 2.132787    63
     lhs                              rhs                       
[1]  {HP Black & Tri-color Ink}    => {ViewSonic Monitor}       
[2]  {ViewSonic Monitor}           => {HP Black & Tri-color Ink}
[3]  {Acer Aspire,HP Laptop,iMac}  => {ViewSonic Monitor}       
[4]  {Dell 2 Desktop}              => {Apple Magic Keyboard}    
[5]  {Apple Magic Keyboard}        => {Dell 2 Desktop}          
[6]  {Apple Magic Keyboard,iMac}   => {ASUS Monitor}            
[7]  {Brother Printer}             => {iPad Pro}                
[8]  {iPad Pro}                    => {Brother Printer}         
[9]  {HP Laptop,ViewSonic Monitor} => {Computer Game}           
[10] {HP Wireless Mouse}           => {Epson Printer}           
     support    confidence lift     count
[1]  0.01113967 0.46428571 2.733711 65   
[2]  0.01113967 0.06559031 2.733711 65   
[3]  0.01045416 0.46212121 2.720966 61   
[4]  0.01439589 0.28378378 2.679415 84   
[5]  0.01439589 0.13592233 2.679415 84   
[6]  0.01148243 0.21824104 2.551977 67   
[7]  0.01028278 0.20547945 2.529478 60   
[8]  0.01028278 0.12658228 2.529478 60   
[9]  0.01233933 0.15652174 2.509078 72   
[10] 0.01165381 0.18630137 2.470610 68   

Rules for categories in corporate transactions

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen
       0.01    0.1    1 none FALSE            TRUE       5    0.05      2
 maxlen target   ext
     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 291 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[17 item(s), 5835 transaction(s)] done [0.00s].
sorting and recoding items ... [17 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 done [0.00s].
writing ... [502 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
set of 479 rules 
set of 502 rules

rule length distribution (lhs + rhs):sizes
  2   3   4   5 
136 228 128  10 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.000   2.000   3.000   3.024   4.000   5.000 

summary of quality measures:
    support          confidence          lift            count       
 Min.   :0.05004   Min.   :0.0763   Min.   :0.9674   Min.   : 292.0  
 1st Qu.:0.05947   1st Qu.:0.3226   1st Qu.:1.0542   1st Qu.: 347.0  
 Median :0.07232   Median :0.4639   Median :1.1448   Median : 422.0  
 Mean   :0.09491   Mean   :0.5123   Mean   :1.1463   Mean   : 553.8  
 3rd Qu.:0.10651   3rd Qu.:0.7248   3rd Qu.:1.2188   3rd Qu.: 621.5  
 Max.   :0.54070   Max.   :0.8735   Max.   :1.5310   Max.   :3155.0  

mining info:
         data ntransactions support confidence
 trans_corcat          5835    0.05       0.01
     lhs                   rhs        support   confidence lift     count
[1]  {Laptops}          => {Desktop}  0.5407027 0.7879620  1.008059 3155 
[2]  {Desktop}          => {Laptops}  0.5407027 0.6917343  1.008059 3155 
[3]  {Monitors}         => {Desktop}  0.4327335 0.7868495  1.006636 2525 
[4]  {Desktop}          => {Monitors} 0.4327335 0.5536067  1.006636 2525 
[5]  {Monitors}         => {Laptops}  0.3797772 0.6905578  1.006345 2216 
[6]  {Laptops}          => {Monitors} 0.3797772 0.5534466  1.006345 2216 
[7]  {Laptops,Monitors} => {Desktop}  0.3076264 0.8100181  1.036276 1795 
[8]  {Desktop,Monitors} => {Laptops}  0.3076264 0.7108911  1.035976 1795 
[9]  {Desktop,Laptops}  => {Monitors} 0.3076264 0.5689382  1.034514 1795 
[10] {Computer Mice}    => {Desktop}  0.2904884 0.7821874  1.000672 1695 
     lhs                   rhs          support confidence     lift count
[1]  {Computer Mice,                                                     
      Keyboard,                                                          
      Laptops,                                                           
      Monitors}         => {Desktop} 0.06272494  0.8735084 1.117501   366
[2]  {Accessories,                                                       
      Keyboard,                                                          
      Monitors}         => {Desktop} 0.05895458  0.8600000 1.100219   344
[3]  {Keyboard,                                                          
      Laptops,                                                           
      Monitors}         => {Desktop} 0.12613539  0.8578089 1.097416   736
[4]  {Accessories,                                                       
      Keyboard,                                                          
      Laptops}          => {Desktop} 0.06152528  0.8568019 1.096128   359
[5]  {Computer Mice,                                                     
      Computer Tablets} => {Desktop} 0.05604113  0.8560209 1.095129   327
[6]  {Computer Mice,                                                     
      Keyboard,                                                          
      Monitors}         => {Desktop} 0.07866324  0.8531599 1.091468   459
[7]  {Accessories,                                                       
      Laptops,                                                           
      Monitors}         => {Desktop} 0.09340189  0.8475894 1.084342   545
[8]  {Computer Cords,                                                    
      Laptops,                                                           
      Monitors}         => {Desktop} 0.05621251  0.8475452 1.084286   328
[9]  {Accessories,                                                       
      Computer Mice,                                                     
      Monitors}         => {Desktop} 0.05724079  0.8455696 1.081758   334
[10] {Computer Mice,                                                     
      Keyboard,                                                          
      Laptops}          => {Desktop} 0.09048843  0.8448000 1.080774   528
     lhs                                 rhs                       
[1]  {Keyboard,Laptops,Monitors}      => {Accessories}             
[2]  {Desktop,Keyboard,Monitors}      => {Accessories}             
[3]  {Keyboard,Monitors}              => {Accessories}             
[4]  {Computer Mice,Laptops,Monitors} => {Accessories}             
[5]  {Computer Cords,Desktop}         => {Keyboard}                
[6]  {Desktop,Keyboard,Laptops}       => {Accessories}             
[7]  {Computer Mice,Desktop,Monitors} => {Accessories}             
[8]  {Accessories,Desktop,Monitors}   => {Keyboard}                
[9]  {Computer Cords}                 => {Mouse and Keyboard Combo}
[10] {Mouse and Keyboard Combo}       => {Computer Cords}          
     support    confidence lift     count
[1]  0.05158526 0.3508159  1.531047 301  
[2]  0.05895458 0.3412698  1.489386 344  
[3]  0.06855184 0.3276003  1.429729 400  
[4]  0.05347044 0.3260188  1.422827 312  
[5]  0.05141388 0.4807692  1.398449 300  
[6]  0.06152528 0.3182624  1.388976 359  
[7]  0.05724079 0.3177926  1.386926 334  
[8]  0.05895458 0.4744828  1.380163 344  
[9]  0.05107112 0.3692689  1.364588 298  
[10] 0.05107112 0.1887270  1.364588 298  

Rules for products in retailers transactions

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen
       0.01    0.1    1 none FALSE            TRUE       5   0.005      2
 maxlen target   ext
     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 20 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[125 item(s), 4000 transaction(s)] done [0.00s].
sorting and recoding items ... [68 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.00s].
writing ... [22 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
set of 22 rules 
set of 22 rules

rule length distribution (lhs + rhs):sizes
 2 
22 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      2       2       2       2       2       2 

summary of quality measures:
    support           confidence           lift            count      
 Min.   :0.005000   Min.   :0.04600   Min.   :0.5283   Min.   :20.00  
 1st Qu.:0.006375   1st Qu.:0.06696   1st Qu.:1.3676   1st Qu.:25.50  
 Median :0.007250   Median :0.10666   Median :1.4819   Median :29.00  
 Mean   :0.007932   Mean   :0.13853   Mean   :1.8501   Mean   :31.73  
 3rd Qu.:0.009062   3rd Qu.:0.19223   3rd Qu.:2.4736   3rd Qu.:36.25  
 Max.   :0.014250   Max.   :0.45763   Max.   :3.6096   Max.   :57.00  

mining info:
         data ntransactions support confidence
 trans_retail          4000   0.005       0.01
     lhs                           rhs                        support
[1]  {CYBERPOWER Gamer Desktop} => {Apple Earpods}            0.01425
[2]  {Apple Earpods}            => {CYBERPOWER Gamer Desktop} 0.01425
[3]  {Apple MacBook Air}        => {Apple Earpods}            0.00975
[4]  {Apple Earpods}            => {Apple MacBook Air}        0.00975
[5]  {3-Button Mouse}           => {Apple Earpods}            0.00925
[6]  {Apple Earpods}            => {3-Button Mouse}           0.00925
[7]  {Samsung Monitor}          => {CYBERPOWER Gamer Desktop} 0.00850
[8]  {CYBERPOWER Gamer Desktop} => {Samsung Monitor}          0.00850
[9]  {Apple Macbook Pro}        => {Apple Earpods}            0.00800
[10] {Apple Earpods}            => {Apple Macbook Pro}        0.00800
     confidence lift      count
[1]  0.16764706 1.3331774 57   
[2]  0.11332008 1.3331774 57   
[3]  0.06643952 0.5283461 39   
[4]  0.07753479 0.5283461 39   
[5]  0.20000000 1.5904573 37   
[6]  0.07355865 1.5904573 37   
[7]  0.23129252 2.7210884 34   
[8]  0.10000000 2.7210884 34   
[9]  0.21768707 1.7311099 32   
[10] 0.06361829 1.7311099 32   
     lhs                                                rhs                        support confidence     lift count
[1]  {iPhone Charger Cable}                          => {Apple MacBook Air}        0.00675  0.4576271 3.118413    27
[2]  {Acer Monitor}                                  => {CYBERPOWER Gamer Desktop} 0.00675  0.3068182 3.609626    27
[3]  {Samsung Monitor}                               => {CYBERPOWER Gamer Desktop} 0.00850  0.2312925 2.721088    34
[4]  {Apple Macbook Pro}                             => {Apple Earpods}            0.00800  0.2176871 1.731110    32
[5]  {Microsoft Wireless Desktop Keyboard and Mouse} => {Apple MacBook Air}        0.00725  0.2132353 1.453051    29
[6]  {3-Button Mouse}                                => {Apple Earpods}            0.00925  0.2000000 1.590457    37
[7]  {Dell Laptop}                                   => {Apple Earpods}            0.00625  0.1689189 1.343292    25
[8]  {CYBERPOWER Gamer Desktop}                      => {Apple Earpods}            0.01425  0.1676471 1.333177    57
[9]  {3-Button Mouse}                                => {iMac}                     0.00550  0.1189189 1.481856    22
[10] {Backlit LED Gaming Keyboard}                   => {iMac}                     0.00500  0.1156069 1.440585    20
     lhs                           rhs                        support
[1]  {Acer Monitor}             => {CYBERPOWER Gamer Desktop} 0.00675
[2]  {CYBERPOWER Gamer Desktop} => {Acer Monitor}             0.00675
[3]  {iPhone Charger Cable}     => {Apple MacBook Air}        0.00675
[4]  {Apple MacBook Air}        => {iPhone Charger Cable}     0.00675
[5]  {Samsung Monitor}          => {CYBERPOWER Gamer Desktop} 0.00850
[6]  {CYBERPOWER Gamer Desktop} => {Samsung Monitor}          0.00850
[7]  {Apple Macbook Pro}        => {Apple Earpods}            0.00800
[8]  {Apple Earpods}            => {Apple Macbook Pro}        0.00800
[9]  {3-Button Mouse}           => {Apple Earpods}            0.00925
[10] {Apple Earpods}            => {3-Button Mouse}           0.00925
     confidence lift     count
[1]  0.30681818 3.609626 27   
[2]  0.07941176 3.609626 27   
[3]  0.45762712 3.118413 27   
[4]  0.04599659 3.118413 27   
[5]  0.23129252 2.721088 34   
[6]  0.10000000 2.721088 34   
[7]  0.21768707 1.731110 32   
[8]  0.06361829 1.731110 32   
[9]  0.20000000 1.590457 37   
[10] 0.07355865 1.590457 37   

Rules for categories in retailers transactions

Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen
       0.01    0.1    1 none FALSE            TRUE       5    0.01      2
 maxlen target   ext
     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 40 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[17 item(s), 4000 transaction(s)] done [0.00s].
sorting and recoding items ... [17 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 done [0.00s].
writing ... [40 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
set of 22 rules 
set of 40 rules

rule length distribution (lhs + rhs):sizes
 2 
40 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      2       2       2       2       2       2 

summary of quality measures:
    support          confidence           lift            count    
 Min.   :0.01025   Min.   :0.03275   Min.   :0.5645   Min.   : 41  
 1st Qu.:0.01250   1st Qu.:0.08498   1st Qu.:0.6138   1st Qu.: 50  
 Median :0.01675   Median :0.12546   Median :0.7535   Median : 67  
 Mean   :0.02275   Mean   :0.15262   Mean   :0.8038   Mean   : 91  
 3rd Qu.:0.03625   3rd Qu.:0.21769   3rd Qu.:0.9706   3rd Qu.:145  
 Max.   :0.05375   Max.   :0.35602   Max.   :1.2459   Max.   :215  

mining info:
         data ntransactions support confidence
 trans_retcat          4000    0.01       0.01
     lhs                    rhs                 support confidence
[1]  {Monitors}          => {Desktop}           0.05375 0.3272451 
[2]  {Desktop}           => {Monitors}          0.05375 0.1881015 
[3]  {Computer Mice}     => {Desktop}           0.03825 0.2875940 
[4]  {Desktop}           => {Computer Mice}     0.03825 0.1338583 
[5]  {Monitors}          => {Laptops}           0.03725 0.2267884 
[6]  {Laptops}           => {Monitors}          0.03725 0.1190096 
[7]  {Keyboard}          => {Desktop}           0.03625 0.2859961 
[8]  {Desktop}           => {Keyboard}          0.03625 0.1268591 
[9]  {Active Headphones} => {Laptops}           0.03625 0.2449324 
[10] {Laptops}           => {Active Headphones} 0.03625 0.1158147 
     lift      count
[1]  1.1452145 215  
[2]  1.1452145 215  
[3]  1.0064531 153  
[4]  1.0064531 153  
[5]  0.7245637 149  
[6]  0.7245637 149  
[7]  1.0008611 145  
[8]  1.0008611 145  
[9]  0.7825317 145  
[10] 0.7825317 145  
     lhs                           rhs       support confidence lift     
[1]  {Accessories}              => {Desktop} 0.01700 0.3560209  1.2459176
[2]  {Monitors}                 => {Desktop} 0.05375 0.3272451  1.1452145
[3]  {Mouse and Keyboard Combo} => {Laptops} 0.02275 0.3204225  1.0237142
[4]  {Computer Cords}           => {Laptops} 0.01150 0.3006536  0.9605546
[5]  {Computer Mice}            => {Desktop} 0.03825 0.2875940  1.0064531
[6]  {Keyboard}                 => {Desktop} 0.03625 0.2859961  1.0008611
[7]  {Computer Headphones}      => {Desktop} 0.01250 0.2577320  0.9019491
[8]  {Active Headphones}        => {Laptops} 0.03625 0.2449324  0.7825317
[9]  {Active Headphones}        => {Desktop} 0.03625 0.2449324  0.8571564
[10] {Monitors}                 => {Laptops} 0.03725 0.2267884  0.7245637
     count
[1]   68  
[2]  215  
[3]   91  
[4]   46  
[5]  153  
[6]  145  
[7]   50  
[8]  145  
[9]  145  
[10] 149  
     lhs                           rhs                        support
[1]  {Accessories}              => {Desktop}                  0.01700
[2]  {Desktop}                  => {Accessories}              0.01700
[3]  {Desktop}                  => {Monitors}                 0.05375
[4]  {Monitors}                 => {Desktop}                  0.05375
[5]  {Mouse and Keyboard Combo} => {Laptops}                  0.02275
[6]  {Laptops}                  => {Mouse and Keyboard Combo} 0.02275
[7]  {Computer Mice}            => {Desktop}                  0.03825
[8]  {Desktop}                  => {Computer Mice}            0.03825
[9]  {Keyboard}                 => {Desktop}                  0.03625
[10] {Desktop}                  => {Keyboard}                 0.03625
     confidence lift     count
[1]  0.35602094 1.245918  68  
[2]  0.05949256 1.245918  68  
[3]  0.18810149 1.145215 215  
[4]  0.32724505 1.145215 215  
[5]  0.32042254 1.023714  91  
[6]  0.07268371 1.023714  91  
[7]  0.28759398 1.006453 153  
[8]  0.13385827 1.006453 153  
[9]  0.28599606 1.000861 145  
[10] 0.12685914 1.000861 145  

Rules visualizations

Available control parameters (with default values):
main     =  Graph for 10 rules
nodeColors   =  c("#66CC6680", "#9999CC80")
nodeCol  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol  =  c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF",  "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF",  "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha    =  0.5
cex  =  1
itemLabels   =  TRUE
labelCol     =  #000000B3
measureLabels    =  FALSE
precision    =  3
layout   =  NULL
layoutParams     =  list()
arrowSize    =  0.5
engine   =  igraph
plot     =  TRUE
plot_options     =  list()
max  =  100
verbose  =  FALSE

Available control parameters (with default values):
main     =  Graph for 10 rules
nodeColors   =  c("#66CC6680", "#9999CC80")
nodeCol  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol  =  c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF",  "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF",  "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha    =  0.5
cex  =  1
itemLabels   =  TRUE
labelCol     =  #000000B3
measureLabels    =  FALSE
precision    =  3
layout   =  NULL
layoutParams     =  list()
arrowSize    =  0.5
engine   =  igraph
plot     =  TRUE
plot_options     =  list()
max  =  100
verbose  =  FALSE

Available control parameters (with default values):
main     =  Graph for 10 rules
nodeColors   =  c("#66CC6680", "#9999CC80")
nodeCol  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol  =  c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF",  "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF",  "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha    =  0.5
cex  =  1
itemLabels   =  TRUE
labelCol     =  #000000B3
measureLabels    =  FALSE
precision    =  3
layout   =  NULL
layoutParams     =  list()
arrowSize    =  0.5
engine   =  igraph
plot     =  TRUE
plot_options     =  list()
max  =  100
verbose  =  FALSE

Available control parameters (with default values):
main     =  Graph for 10 rules
nodeColors   =  c("#66CC6680", "#9999CC80")
nodeCol  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol  =  c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF",  "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF",  "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha    =  0.5
cex  =  1
itemLabels   =  TRUE
labelCol     =  #000000B3
measureLabels    =  FALSE
precision    =  3
layout   =  NULL
layoutParams     =  list()
arrowSize    =  0.5
engine   =  igraph
plot     =  TRUE
plot_options     =  list()
max  =  100
verbose  =  FALSE

Available control parameters (with default values):
main     =  Scatter plot for 1752 rules
engine   =  default
pch  =  19
cex  =  0.5
xlim     =  NULL
ylim     =  NULL
zlim     =  NULL
alpha    =  NULL
col  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage  =  TRUE
jitter   =  NA
verbose  =  FALSE

Available control parameters (with default values):
main     =  Scatter plot for 502 rules
engine   =  default
pch  =  19
cex  =  0.5
xlim     =  NULL
ylim     =  NULL
zlim     =  NULL
alpha    =  NULL
col  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage  =  TRUE
jitter   =  NA
verbose  =  FALSE

Available control parameters (with default values):
main     =  Scatter plot for 22 rules
engine   =  default
pch  =  19
cex  =  0.5
xlim     =  NULL
ylim     =  NULL
zlim     =  NULL
alpha    =  NULL
col  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage  =  TRUE
jitter   =  NA
verbose  =  FALSE

Available control parameters (with default values):
main     =  Scatter plot for 40 rules
engine   =  default
pch  =  19
cex  =  0.5
xlim     =  NULL
ylim     =  NULL
zlim     =  NULL
alpha    =  NULL
col  =  c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF",  "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF",  "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage  =  TRUE
jitter   =  NA
verbose  =  FALSE

Andreu Oros, Sergi Pallice, Joël Ribera

2019-07-24